title: A new hybrid system of QSAR models for predicting bioconcentration factors (BCF).
abstract: The aim was to develop a reliable and practical quantitative structure-activity relationship (QSAR) model validated by strict conditions for predicting bioconcentration factors (BCF). We built up several QSAR models starting from a large data set of 473 heterogeneous chemicals, based on multiple linear regression (MLR), radial basis function neural network (RBFNN) and support vector machine (SVM) methods. To improve the results, we also applied a hybrid model, which gave better prediction than single models. All models were statistically analysed using strict criteria, including an external test set. The outliers were also examined to understand better in which cases large errors were to be expected and to improve the predictive models. The models offer more robust tools for regulatory purposes, on the basis of the statistical results and the quality check on the input data.
authors.affiliation: Department of Chemistry, Lanzhou University, Lanzhou, China.
authors.forename: Chunyan
authors.initials: C
authors.lastname: Zhao
authors.forename: Elena
authors.initials: E
authors.lastname: Boriani
authors.forename: Antonio
authors.initials: A
authors.lastname: Chana
authors.forename: Alessandra
authors.initials: A
authors.lastname: Roncaglioni
authors.forename: Emilio
authors.initials: E
authors.lastname: Benfenati
mesh_terms: D004784:Environmental Monitoring / Q000379:methods*; D016014:Linear Models; D008956:Models, Chemical; D016571:Neural Networks, Computer; D021281:Quantitative Structure-Activity Relationship; D015203:Reproducibility of Results